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Tech Lead Generative AI Engineer Budget :- Open Market Rate We are looking for an exceptional Tech Lead Generative AI Engineer to drive the design, development, and delivery of enterprise-grade AI-powered products and platforms. In this role, you will combine deep expertise in Generative AI, large language models (LLMs), and cloud- native Azure PaaS services with hands-on engineering using .NET and Python. You will lead a team of engineers, set technical direction, and deliver cutting-edge GenAI solutions that create measurable business impact. This is a senior, hands-on leadership role you will architect and code alongside your team, mentor engineers, and act as the AI technical authority across product squads. Key Responsibilities Technical Leadership Lead architecture and end-to-end delivery of production GenAI applications, including RAG pipelines, AI agents, and LLM-powered features. Define and enforce engineering standards, best practices, and design patterns across AI and backend systems. Drive technical decision-making model selection, infrastructure choices, build vs buy tradeoffs with a clear rationale. Conduct architecture reviews and provide technical mentorship to a team of 5 8 engineers. Collaborate with Product, Data Science, and Platform teams to shape the AI product roadmap. Generative AI Engineering Design and build scalable RAG systems chunking strategies, vector stores, hybrid search, reranking, and evaluation pipelines. Develop and deploy AI agent frameworks using ReAct, tool use, and multi-agent orchestration patterns. Implement fine-tuning workflows (LoRA, QLoRA) and prompt engineering strategies for production use cases. Build robust evaluation frameworks: LLM-as-judge, hallucination detection, RAGAS, and A/B testing for AI outputs. Apply guardrails, safety checks, and responsible AI practices across all GenAI products. Azure PaaS & Cloud Engineering Architect and operate GenAI solutions on Azure Azure OpenAI Service, Azure AI Search, Azure Machine Learning, and Azure Kubernetes Service (AKS). Design event-driven and microservices architectures using Azure Service Bus, Event Grid, Azure Functions, and API Management. Manage infrastructure as code using Bicep or Terraform; implement CI/CD pipelines via Azure DevOps. Ensure security, compliance, and cost efficiency across Azure-hosted AI workloads. .NET & Python Development Build high-performance backend services and APIs using .NET 8 / ASP.NET Core for enterprise integrations. Develop AI/ML pipelines, data processing, and orchestration logic in Python (FastAPI, LangChain, LangGraph, Semantic Kernel). Design and maintain polyglot persistence layers Azure SQL, Cosmos DB, Azure Cache for Redis, and vector databases (Azure AI Search, pgvector). Enforce clean code principles: SOLID, DDD, TDD, and code review discipline. Required Skills & Experience Must-have 8+ years of software engineering experience, with 2+ years in a tech lead or principal engineer role. Hands-on experience designing and shipping production Generative AI applications (RAG, LLM APIs, agents). Strong proficiency in Python for AI/ML engineering (LangChain, LangGraph, Semantic Kernel, or similar frameworks). Strong proficiency in .NET / C# for enterprise backend and API development. Deep expertise in Azure PaaS: Azure OpenAI, Azure AI Search, Azure ML, AKS, Azure Functions, Service Bus. Experience with vector databases and semantic search (Azure AI Search, pgvector, Pinecone, Weaviate, or Chroma). Proven ability to lead and grow engineering teams; track record of technical mentorship. Strong system design skills able to design for scale, reliability, and cost-efficiency in cloud-native environments. Experience with fine-tuning LLMs using LoRA / QLoRA and managing training pipelines on Azure ML. Experience with multi-agent frameworks LangGraph, AutoGen, CrewAI, or similar. Knowledge of model quantization and LLM inference optimization (vLLM, ONNX Runtime, TGI). Experience with DSPy, prompt evaluation frameworks, or automated prompt optimization. Azure certifications (AI-102, AZ-204, AZ-305) or equivalent cloud credentials. Experience with multimodal AI models (GPT-4o, vision, audio) or document intelligence pipelines. Tech Stack AI / LLMs Azure OpenAI (GPT-4o, Ada), LangChain, LangGraph, Semantic Kernel, RAGAS, LlamaIndex Cloud Azure Azure AI Search, Azure ML, AKS, Functions, Service Bus, Event Grid, APIM, Bicep, DevOps Languages .NET 8 / C#, Python 3.11+ Frameworks ASP.NET Core, FastAPI, Entity Framework Core Data & Storage Azure SQL, Cosmos DB, Redis Cache, Azure Data Lake, pgvector DevOps & Tooling Azure DevOps, GitHub Actions, Docker, Kubernetes, Terraform Observability Azure Monitor, Application Insights, OpenTelemetry, LangSmith What We Offer Opportunity to build AI-first products from .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.